Bayesian representation of prior information and MCMC method in microwave imaging / 生物医学工程学杂志
Journal of Biomedical Engineering
; (6): 1108-1111, 2005.
Article
em Zh
| WPRIM
| ID: wpr-238266
Biblioteca responsável:
WPRO
ABSTRACT
Microwave imaging for dielectric objects was considered in this paper. Applying Bayesian approach to represent prior information about permittivity distribution of observed object by prior probability density and combine measurements information of scattering field, we obtained posterior probability density that included synthetic information about the observed object. And then, Gibbs sampler, one of Markov Chain Monte Carlo method, was used to sample the posterior probability density. The sample mean was regarded as an evaluation of the permittivity distribution. The results of simulation imaging with "blocky" objects showed that this set of methods made good use of information and had the advantages of feasibility and very strong anti-noise ability. In addition,it is capable of describing (definite or indefinite) prior information in a convenient and controllable way, as well as capable of giving the "complete" solution, i.e., the occurrence probability of every permittivity distribution.
Texto completo:
1
Base de dados:
WPRIM
Assunto principal:
Simulação por Computador
/
Processamento de Imagem Assistida por Computador
/
Método de Monte Carlo
/
Cadeias de Markov
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Teorema de Bayes
/
Diagnóstico
/
Métodos
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Micro-Ondas
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Modelos Teóricos
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Neoplasias
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Prognostic_studies
Idioma:
Zh
Revista:
Journal of Biomedical Engineering
Ano de publicação:
2005
Tipo de documento:
Article